Evaluating LLMs vs Encoders for Biomedical Recognition
Comparing state-of-the-art approaches for identifying medical entities in text
This research evaluates whether the latest Large Language Models outperform specialized encoder models for biomedical named entity recognition (NER) - a critical component of medical information extraction.
- Assessed performance of various models in identifying medical concepts like drugs and genes in clinical text
- Compared traditional transformer-based encoders (BERT) against newer LLM approaches
- Evaluated strengths and limitations of each approach for medical applications
- Examined practical implementation considerations including computational requirements
Why it matters: Accurate biomedical NER enables better knowledge discovery, information retrieval, and clinical decision support systems. Understanding which technologies excel at this task directly impacts the development of effective medical AI systems.